Tackling Imbalanced Datasets in Classification Problems
When I started working with Imbalanced Data, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how i handle imbalanced datasets in classification problems. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Imbalanced Datasets I still remember the first time I encountered an imbalanced dataset in a classification problem. I was working on a fraud detection model, and my initial results showed a whopping 99 percent accuracy. Sounds great, right? But as I dug deeper, I realized that my model was predicting every single instance as non-fraud. The model was essentially useless, as it was unable to detect any fraudulent cases. This experience taught me a valuable lesson: accuracy is not always the best metric, especially when dealing with imbalanced datasets. The Problem with Imbalanced Datasets Imbalance...